Clique-based Approach Research Articles

Connectivity-Preserving Formation Control via Clique-Based Approach Without Prior Assignment

Connectivity-Preserving Formation Control via Clique-Based Approach Without Prior Assignment

Transcriptional Hubs Within Cliques in Ensemble Hi-C Chromatin Interaction Networks.

Chromatin conformation capture technologies permit the study of chromatin spatial organization on a genome-wide scale at a variety of resolutions. Despite the increasing precision and resolution of high-throughput chromatin conformation capture (Hi-C) methods, it remains challenging to conclusively link transcriptional activity to spatial organizational phenomena. We have developed a clique-based approach for analyzing Hi-C data that helps identify chromosomal hotspots that feature considerable enrichment of chromatin annotations for transcriptional start sites and, building on previously published work, show that these chromosomal hotspots are not only significantly enriched in RNA polymerase II binding sites as identified by the ENCODE project, but also identify a noticeable increase in FANTOM5 and GTEx transcription within our identified cliques across a variety of tissue types. From the obtained data, we surmise that our cliques are a suitable method for identifying transcription factories in Hi-C data, and outline further extensions to the method that may make it useful for locating regions of increased transcriptional activity in datasets where in-depth expression or polymerase data may not be available.

Intelligent Reflecting Surface for Multi-Path Beam Routing With Active/Passive Beam Splitting and Combining

Intelligent reflecting surface (IRS) can be densely deployed in wireless networks to significantly enhance the communication channels. In this letter, we consider the downlink transmission from a multi-antenna base station (BS) to a single-antenna user, by exploiting the cooperative passive beamforming (CPB) and line-of-sight (LoS) path diversity gains of multi-IRS signal reflection. Unlike existing works where only one single multi-IRS reflection path from the BS to user is selected, we propose a new and more general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-path beam routing</i> scheme. Specifically, the BS sends the user’s information signal via multiple orthogonal active beams (termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">active beam splitting</i> ), which point towards different IRSs. Then, these beamed signals are subsequently reflected by selected IRSs via their CPB in different paths, and finally coherently combined at the user’s receiver (thus named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">passive beam combining</i> ). For this scheme, we formulate a new multi-path beam routing design problem to jointly optimize the number of IRS reflection paths, the selected IRSs for each of the reflection paths, the active/passive beamforming at the BS/each selected IRS, as well as the BS’s power allocation over different active beams, so as to maximize the received signal power at the user. To solve this challenging problem, we first derive the optimal BS/IRS beamforming and BS power allocation for a given set of reflection paths. The clique-based approach in graph theory is then applied to solve the remaining multi-path selection problem efficiently. Simulation results show that our proposed multi-path beam routing scheme significantly outperforms its conventional single-path beam routing special case.

A clique-based approach for co-location pattern mining

Co-location pattern mining refers to the task of discovering the group of features (geographic object types) whose instances (geographic objects) are frequently located close together in a geometric space. Current approaches on this topic adopt a prevalence threshold (a measure of a user's interest in a pattern) to generate prevalent co-location patterns. However, in practice, it is not easy to specify a suitable prevalence threshold. Thus, users have to repeatedly execute the program to find a suitable prevalence threshold. Besides, the efficiency of these approaches is limited because of the expensive cost of identifying row-instances of co-location patterns. In this paper, we propose a novel clique-based approach for discovering complete and correct prevalent co-location patterns. The proposed approach avoids identifying row-instances of co-location patterns thus making it much easier to find a proper prevalence threshold. First, two efficient schemas are designed to generate complete and correct cliques. Next, these cliques are transformed into a hash structure which is independent of the prevalence threshold. Finally, the prevalence of each co-location pattern is efficiently calculated using the hash structure. The experiments on both real and synthetic datasets show the efficiency and effectiveness of our proposed approaches.

Capacity-Aware and Delay-Guaranteed Resilient Controller Placement for Software-Defined WANs

Currently, one of the main enablers for network evolution is software-defined networking (SDN), where the control plane is decoupled from the data plane. A controller, as a (logically) centralized entity in the control plane, is the Achilles’ heel of SDN resilience since its failure would affect the proper functioning of the entire network. The resilience of the control plane is strongly linked to the controller placement problem, which deals with the positioning and assignment of controllers to the forwarding devices (i.e., switches). A resilient controller placement problem needs to assign more than one controller to a switch while it satisfies certain quality of service requirements. In this paper, we propose a solution for such a problem that, unlike most of the former studies, takes both the switch-controller /inter-controller latency requirements and the capacity of the controllers into account to meet the traffic load of switches. The proposed algorithms, one of which has a polynomial-time complexity, adopt a clique-based approach in graph theory to find high-quality solutions heuristically. It is evaluated with real wide area network (WAN) topologies and the corresponding results are extensively analyzed. The resultant studies equip the service providers with helpful insights into the design of a resilient software-defined WAN.

A Space-Time Graph Optimization Approach Based on Maximum Cliques for Action Detection

We present an efficient action detection method that takes a space-time (ST) graph optimization approach for real-world videos. Given an ST graph representing the entire action video, our method identifies a maximum-weight connected subgraph (MWCS) indicating an action region by applying an optimization approach based on clique information. We define an energy function based on maximum weight cliques for subregions of the graph and formulate it using an optimization problem that can be represented as a linear system. Our energy function includes the maximum and connectivity properties for finding the MWCS, and its optimization solution indicates the probability of belonging to the maximum subgraph for each node. Our graph optimization method efficiently solves the detection problem by applying the clique-based approach and simple linear system solver. We demonstrate that our detection method results in a more accurate localization compared with conventional methods through our experimental results with real-world data sets, such as the Hollywood and MSR action data sets. We also show that our method outperforms the state-of-the-art methods of action detection.

Solving the winner determination problem via a weighted maximum clique heuristic

Combinatorial auctions (CAs) where bidders can bid on combinations of items is an important model in many application areas. CAs attract more and more attention in recent years due to its relevance to fast growing electronic business applications. In this paper, we study the winner determination problem (WDP) in CAs which is known to be NP-hard and thus computationally difficult in the general case. We develop a solution approach for the WDP by recasting the WDP into the maximum weight clique problem (MWCP) and solving the transformed problem with a recent heuristic dedicated to the MWCP. The computational experiments on a large range of 530 benchmark instances show that the clique-based approach for the WDP not only outperforms the current best performing WDP heuristics in the literature both in terms of solution quality and computation efficiency, but also competes very favorably with the powerful CPLEX solver.

A Clique-Based Approach to the Identification of Common Gene Association Sub-Networks

We developed a computational framework to identify common gene association sub-network. This framework combines graphical lasso model, graph product and a replicator equation based clique solver. We applied this method to find common stress responsive sub-networks from two related Deinococcus-Thermus bacterial species.

Clique-based facets for the precedence constrained knapsack problem

We consider a knapsack problem with precedence constraints imposed on pairs of items, known as the precedence constrained knapsack problem (PCKP). This problem has applications in manufacturing and mining, and also appears as a subproblem in decomposition techniques for network design and related problems. We present a new approach for determining facets of the PCKP polyhedron based on clique inequalities. A comparison with existing techniques, that lift knapsack cover inequalities for the PCKP, is also presented. It is shown that the clique-based approach generates facets that cannot be found through the existing cover-based approaches, and that the addition of clique-based inequalities for the PCKP can be computationally beneficial, for both PCKP instances arising in real applications, and applications in which PCKP appears as an embedded structure.

Research on EEG Features Extraction Based on Clique

Abstract Using cluster analysis method for feature extraction, with the advantages of does not depend on the defined class to training and learning. We use clique method, through analyze the motor imagery EEG, and with the help of similarity and Dissimilarity degree that defined in this paper, we design the feature extraction step, through test, clique-based approach designed to bring a good feature to extract EEG features.

Mining relevant information on the Web: a clique-based approach

The role of information management and retrieval in production processes has been gaining in importance in recent years. In this context, the ability to search for and quickly find the small piece of information needed from the huge amount of information available has crucial importance. One category of tools devoted to such a task is represented by search engines. Satisfying the basic needs of the Web user has led to the research of new tools that aim at helping more sophisticated users (communities, companies, interest groups) with more elaborate methods. An example is the use of clustering and classification algorithms or other specific data mining techniques. In such a context, the proper use of a thematic search engine is a crucial tool in supporting and orienting many activities. Several practical and theoretical problems arise in developing such tools, and we try to face some of these in this paper, extending previous work on Web mining. Here we consider two related problems: how to select an appropriate set of keywords for a thematic engine taking into account the semantic and linguistic extensions of the search context, and how to select and rank a subset of relevant pages given a set of search keywords. Both problems are solved using the same framework, based on a graph representation of the available information and on the search of particular node subsets of such a graph. Such subsets are effectively identified by a maximum-weight clique algorithm customized ad hoc for specific problems. The methods have been developed in the framework of a funded research project for the development of new Web search tools, they have been tested on real data, and are currently being implemented in a prototypal thematic search engine. The Web mining method presented in this paper can be applied to Web-based design and manufacturing.

Feature-based and Clique-based User Models for Movie Selection: A Comparative Study

The huge amount of information available in the currently evolving world wide information infrastructure at any one time can easily overwhelm end-users. One way to address the information explosion is to use an ’information filtering agent‘ which can select information according to the interest and/or need of an end-user. However, at present few information filtering agents exist for the evolving world wide multimedia information infrastructure. In this study, we evaluate the use of feature-based approaches to user modeling with the purpose of creating a filtering agent for the video-on-demand application. We evaluate several feature and clique-based models for 10 voluntary subjects who provided ratings for the movies. Our preliminary results suggest that feature-based selection can be a useful tool to recommend movies according to the taste of the user and can be as effective as a movie rating expert. We compare our feature-based approach with a clique-based approach, which has advantages where information from other users is available.